{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/rpm47/Dropbox/0001 Academic Projects/Completed/0171 Parasecurity and Education/Replication/LOG Analysis Pew 2025 09 22.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 Sep 2025, 16:36:43
{txt}
{com}. 
. ********************************************************************************
. *                                                               Data Load and Clean                                                        *
. ********************************************************************************
. 
. use "${c -(}MyProject{c )-}/Pew Data Minimal.dta"
{txt}
{com}. 
. /* The dataset provided is truncated. The original is available from the Roper
> Center, study 31118243, "Pew Research Center: American Trends Panel Wave 82",
> February 1-7, 2021 */
. 
. // Create age category variable, removing missing values coded as 99
. 
. clonevar agecat = F_AGECAT
{txt}
{com}. recode agecat (99=.)
{txt}(10 changes made to {bf:agecat})

{com}. 
. // Recode China power containment priority variable
. 
. clonevar prcpower = GAP21_35
{txt}(1,309 missing values generated)

{com}. lab var prcpower "Containing power and influence of China"
{txt}
{com}. recode prcpower (99=.)
{txt}(14 changes made to {bf:prcpower})

{com}. recode prcpower (2/3=2)
{txt}(62 changes made to {bf:prcpower})

{com}. lab def GAP21_35 2 "No/Some priority", modify
{txt}
{com}. 
. // Label the US-China relationship variable
. lab var GAP21Q40 "China relationship with U.S."
{txt}
{com}. recode GAP21Q40 (99=.)
{txt}(27 changes made to {bf:GAP21Q40})

{com}. 
. // Create binary variable for views on admitting Chinese students to universities
. clonevar goodbaduni = GAP21Q46
{txt}
{com}. lab var goodbaduni "Good or bad to admit Chinese students" 
{txt}
{com}. lab def goodbadunilab 0 "Bad" 1 "Good"
{txt}
{com}. recode goodbaduni (99=.) (2=0)
{txt}(490 changes made to {bf:goodbaduni})

{com}. lab val goodbaduni goodbadunilab
{txt}
{com}. 
. // Recode party identification into three categories
. clonevar pid3 = F_PARTY_
{txt}
{com}. recode pid3 (2=12) (3/4=2) (99=.)
{txt}(1,841 changes made to {bf:pid3})

{com}. recode pid3 (12 = 3)
{txt}(912 changes made to {bf:pid3})

{com}. lab def pid3lab 1 "Republican" 2 "Independent" 3 "Democrat"
{txt}
{com}. lab val pid3 pid3lab
{txt}
{com}. 
. // Set up gender variable (keeping original coding)
. clonevar gender_trinary = F_GENDER
{txt}
{com}. recode gender_trinary (99=.)
{txt}(6 changes made to {bf:gender_trinary})

{com}. lab def F_GENDER 1 "Male" 3 "Non-binary / other definition", modify
{txt}
{com}. 
. // Education level variable
. clonevar education = F_EDUC_1
{txt}
{com}. recode education (99=.)
{txt}(5 changes made to {bf:education})

{com}. lab var education "Education Level"
{txt}
{com}. 
. // Create binary indicator for born in USA
. gen borninusa = (F_BIRTHP == 1)
{txt}
{com}. replace borninusa = . if F_BIRTHP == 99
{txt}(12 real changes made, 12 to missing)

{com}. lab var borninusa "Born in USA"
{txt}
{com}. 
. // Race/ethnicity and household income variables
. clonevar raceethn = F_RACETH
{txt}
{com}. clonevar hhi_cat = F_INC_TI
{txt}
{com}. 
. // Create variable for support/opposition to limiting Chinese students
. clonevar supportopposeprc = GAP21Q47
{txt}
{com}. lab var supportopposeprc "View on limiting Chinese students"
{txt}
{com}. recode supportopposeprc (99 = .)
{txt}(40 changes made to {bf:supportopposeprc})

{com}. revrs supportopposeprc, replace 
{txt}
{com}. 
. // Modify education label for clarity
. lab def F_EDUC_1 5 "College grad/some postgrad", modify
{txt}
{com}. 
. // Create average thermometer rating across Asian countries (China, India, Japan, North Korea)
. egen avgtherm = rowmean(thermchi thermind thermjap thermnko) if thermchi <= 100 & ///
>                                 thermind <= 100 & thermjap <= 100 & thermnko <= 100
{txt}(1,293 missing values generated)

{com}. 
. // Calculate net thermometer ratings (country rating minus average)
. gen china_net_mean = thermchi - avgtherm
{txt}(1,293 missing values generated)

{com}. gen japan_net_mean = thermjap - avgtherm
{txt}(1,293 missing values generated)

{com}. lab var china_net_mean "China net thermometer"
{txt}
{com}. 
. // Create friendliness/enemy relationship variable      (DTR with China)                        
. clonevar frenemy = GAP21Q40
{txt}(27 missing values generated)

{com}. lab var frenemy "Describe US-China Relationship"
{txt}
{com}. 
. 
. ********************************************************************************
. *                                                       Descriptive Statistics                                                     *
. ********************************************************************************
. 
. 
. dtable i.agecat i.gender_trinary i.raceethn i.education i.pid3                                  ///
> ,       title("Demographics For Pew Survey \label{c -(}tab:summarypew{c )-}")                                     ///
>         export("${c -(}MyProject{c )-}/APPENDIX Table A2.tex", replace tableonly) 
{res}
{smcl}
{reset}{...}
{p}Demographics For Pew Survey \label{tab:summarypew}{p_end}
{hline 31}{c -}{hline 13}
{space 31} {space 3}Summary{space 3}
{hline 31}{c -}{hline 13}
N{space 30} {space 8}{result:2,596}
Age category{space 19} {space 13}
  18-29{space 24} {space 2}{result:301 (11.6%)}
  30-49{space 24} {space 2}{result:849 (32.8%)}
  50-64{space 24} {space 2}{result:756 (29.2%)}
  65+{space 26} {space 2}{result:680 (26.3%)}
Gender{space 25} {space 13}
  Male{space 25} {result:1,149 (44.4%)}
  A woman{space 22} {result:1,417 (54.7%)}
  Non-binary / other definition {space 4}{result:24 (0.9%)}
Race-Ethnicity{space 17} {space 13}
  White non-Hispanic{space 11} {result:1,717 (66.1%)}
  Black non-Hispanic{space 11} {space 3}{result:248 (9.6%)}
  Hispanic{space 21} {space 2}{result:395 (15.2%)}
  Other{space 24} {space 4}{result:87 (3.4%)}
  Asian non-Hispanic{space 11} {space 3}{result:125 (4.8%)}
  Refused{space 22} {space 4}{result:24 (0.9%)}
Education Level{space 16} {space 13}
  Less than high school{space 8} {space 3}{result:135 (5.2%)}
  High school graduate{space 9} {space 2}{result:679 (26.2%)}
  Some college, no degree{space 6} {space 2}{result:442 (17.1%)}
  Associate's degree{space 11} {space 3}{result:186 (7.2%)}
  College grad/some postgrad{space 3} {space 2}{result:629 (24.3%)}
  Postgraduate{space 17} {space 2}{result:520 (20.1%)}
Party{space 26} {space 13}
  Republican{space 19} {space 2}{result:755 (29.3%)}
  Independent{space 18} {space 2}{result:909 (35.3%)}
  Democrat{space 21} {space 2}{result:912 (35.4%)}
{hline 31}{c -}{hline 13}
{res}{txt}{p 0 1 2}
(collection {res:DTable} exported to file {browse "/Users/rpm47/Dropbox/0001 Academic Projects/Completed/0171 Parasecurity and Education/Replication/~/Dropbox/0001 Academic Projects/Completed/0171 Parasecurity and Education/Replication/APPENDIX Table A2.tex":~/Dropbox/0001 Academic Projects/Completed/0171 Parasecurity and Education/Replication/APPENDIX Table A2.tex})
{p_end}

{com}. 
. ********************************************************************************
. *                                                       Regression Analysis                                                        *
. ********************************************************************************
. 
. * * * * * Support for International Students (Dichotomous DV)
. 
. * * * * OLS for ease of interpretation
. 
. // Model 1: Base model predicting support for international students
. reg goodbaduni i.pid3 i.agecat b2.gender_trinary i.education borninusa i.raceethn, robust

{txt}Linear regression                               Number of obs     = {res}     2,516
                                                {txt}F(18, 2497)       =  {res}    20.61
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1228
                                                {txt}Root MSE          =    {res} .35903

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    goodbaduni{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      t{col 64}   P>|t|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2} .1284383{col 44}{space 2} .0212347{col 55}{space 1}    6.05{col 64}{space 3}0.000{col 72}{space 4} .0867988{col 85}{space 3} .1700778
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2} .2112263{col 44}{space 2} .0196445{col 55}{space 1}   10.75{col 64}{space 3}0.000{col 72}{space 4}  .172705{col 85}{space 3} .2497476
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2} -.053215{col 44}{space 2} .0186385{col 55}{space 1}   -2.86{col 64}{space 3}0.004{col 72}{space 4}-.0897635{col 85}{space 3}-.0166665
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}-.1190719{col 44}{space 2} .0215575{col 55}{space 1}   -5.52{col 64}{space 3}0.000{col 72}{space 4}-.1613444{col 85}{space 3}-.0767995
{txt}{space 26}65+  {c |}{col 32}{res}{space 2}-.1367377{col 44}{space 2} .0225985{col 55}{space 1}   -6.05{col 64}{space 3}0.000{col 72}{space 4}-.1810514{col 85}{space 3}-.0924239
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .0025625{col 44}{space 2} .0147527{col 55}{space 1}    0.17{col 64}{space 3}0.862{col 72}{space 4}-.0263663{col 85}{space 3} .0314914
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2} .0100176{col 44}{space 2} .0616701{col 55}{space 1}    0.16{col 64}{space 3}0.871{col 72}{space 4}-.1109123{col 85}{space 3} .1309474
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2}  .042504{col 44}{space 2} .0419866{col 55}{space 1}    1.01{col 64}{space 3}0.311{col 72}{space 4} -.039828{col 85}{space 3} .1248361
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2} .0974696{col 44}{space 2} .0423982{col 55}{space 1}    2.30{col 64}{space 3}0.022{col 72}{space 4} .0143304{col 85}{space 3} .1806088
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} .1179925{col 44}{space 2} .0475619{col 55}{space 1}    2.48{col 64}{space 3}0.013{col 72}{space 4} .0247278{col 85}{space 3} .2112572
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2} .1352634{col 44}{space 2} .0407769{col 55}{space 1}    3.32{col 64}{space 3}0.001{col 72}{space 4} .0553034{col 85}{space 3} .2152234
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2} .2118926{col 44}{space 2} .0399113{col 55}{space 1}    5.31{col 64}{space 3}0.000{col 72}{space 4}   .13363{col 85}{space 3} .2901552
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2}-.0775275{col 44}{space 2} .0203657{col 55}{space 1}   -3.81{col 64}{space 3}0.000{col 72}{space 4}-.1174629{col 85}{space 3}-.0375921
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2}-.0279136{col 44}{space 2} .0261833{col 55}{space 1}   -1.07{col 64}{space 3}0.286{col 72}{space 4}-.0792568{col 85}{space 3} .0234297
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2}-.0027491{col 44}{space 2} .0224291{col 55}{space 1}   -0.12{col 64}{space 3}0.902{col 72}{space 4}-.0467306{col 85}{space 3} .0412325
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.0241644{col 44}{space 2} .0423341{col 55}{space 1}   -0.57{col 64}{space 3}0.568{col 72}{space 4}-.1071779{col 85}{space 3} .0588492
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2} -.039334{col 44}{space 2}  .026481{col 55}{space 1}   -1.49{col 64}{space 3}0.138{col 72}{space 4}-.0912611{col 85}{space 3}  .012593
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-.0987986{col 44}{space 2} .1093632{col 55}{space 1}   -0.90{col 64}{space 3}0.366{col 72}{space 4}-.3132504{col 85}{space 3} .1156533
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .7489768{col 44}{space 2} .0500666{col 55}{space 1}   14.96{col 64}{space 3}0.000{col 72}{space 4} .6508004{col 85}{space 3} .8471532
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est sto m1
{txt}
{com}. local m1_N = e(N)
{txt}
{com}. 
. // Model 2: Add China power containment priority variable
. reg goodbaduni i.pid3 i.agecat  b2.gender_trinary i.education borninusa i.raceethn  b2.prcpower, robust

{txt}Linear regression                               Number of obs     = {res}     1,240
                                                {txt}F(19, 1220)       =  {res}    10.30
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1261
                                                {txt}Root MSE          =    {res} .35547

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    goodbaduni{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      t{col 64}   P>|t|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2} .0892577{col 44}{space 2}  .029623{col 55}{space 1}    3.01{col 64}{space 3}0.003{col 72}{space 4} .0311401{col 85}{space 3} .1473753
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2} .1557529{col 44}{space 2}  .028074{col 55}{space 1}    5.55{col 64}{space 3}0.000{col 72}{space 4} .1006742{col 85}{space 3} .2108316
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2}-.0440348{col 44}{space 2} .0271323{col 55}{space 1}   -1.62{col 64}{space 3}0.105{col 72}{space 4} -.097266{col 85}{space 3} .0091964
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}-.0934854{col 44}{space 2} .0309791{col 55}{space 1}   -3.02{col 64}{space 3}0.003{col 72}{space 4}-.1542636{col 85}{space 3}-.0327072
{txt}{space 26}65+  {c |}{col 32}{res}{space 2}-.1161517{col 44}{space 2} .0336749{col 55}{space 1}   -3.45{col 64}{space 3}0.001{col 72}{space 4}-.1822188{col 85}{space 3}-.0500845
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .0146797{col 44}{space 2}  .020776{col 55}{space 1}    0.71{col 64}{space 3}0.480{col 72}{space 4} -.026081{col 85}{space 3} .0554404
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2} .0887084{col 44}{space 2} .0324195{col 55}{space 1}    2.74{col 64}{space 3}0.006{col 72}{space 4} .0251042{col 85}{space 3} .1523126
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2} .0051192{col 44}{space 2} .0565358{col 55}{space 1}    0.09{col 64}{space 3}0.928{col 72}{space 4}-.1057989{col 85}{space 3} .1160374
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2} .0686469{col 44}{space 2} .0565385{col 55}{space 1}    1.21{col 64}{space 3}0.225{col 72}{space 4}-.0422767{col 85}{space 3} .1795704
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} .0458177{col 44}{space 2} .0652123{col 55}{space 1}    0.70{col 64}{space 3}0.482{col 72}{space 4}-.0821231{col 85}{space 3} .1737585
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2} .0833779{col 44}{space 2} .0549814{col 55}{space 1}    1.52{col 64}{space 3}0.130{col 72}{space 4}-.0244907{col 85}{space 3} .1912464
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2} .1741309{col 44}{space 2} .0528721{col 55}{space 1}    3.29{col 64}{space 3}0.001{col 72}{space 4} .0704005{col 85}{space 3} .2778613
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2}-.0531883{col 44}{space 2} .0304999{col 55}{space 1}   -1.74{col 64}{space 3}0.081{col 72}{space 4}-.1130265{col 85}{space 3} .0066498
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2}-.0274895{col 44}{space 2} .0374246{col 55}{space 1}   -0.73{col 64}{space 3}0.463{col 72}{space 4}-.1009133{col 85}{space 3} .0459342
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2} .0214985{col 44}{space 2} .0318211{col 55}{space 1}    0.68{col 64}{space 3}0.499{col 72}{space 4}-.0409315{col 85}{space 3} .0839286
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.0254221{col 44}{space 2} .0613761{col 55}{space 1}   -0.41{col 64}{space 3}0.679{col 72}{space 4}-.1458365{col 85}{space 3} .0949923
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2}  -.04258{col 44}{space 2} .0414135{col 55}{space 1}   -1.03{col 64}{space 3}0.304{col 72}{space 4}-.1238297{col 85}{space 3} .0386696
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-.0599957{col 44}{space 2} .1521537{col 55}{space 1}   -0.39{col 64}{space 3}0.693{col 72}{space 4}-.3585076{col 85}{space 3} .2385161
{txt}{space 30} {c |}
{space 22}prcpower {c |}
{space 17}Top priority  {c |}{col 32}{res}{space 2}-.0982055{col 44}{space 2} .0211831{col 55}{space 1}   -4.64{col 64}{space 3}0.000{col 72}{space 4}-.1397649{col 85}{space 3}-.0566461
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .8334584{col 44}{space 2} .0689973{col 55}{space 1}   12.08{col 64}{space 3}0.000{col 72}{space 4} .6980918{col 85}{space 3} .9688249
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est sto m2
{txt}
{com}. local m2_N = e(N)
{txt}
{com}. 
. // Model 2 with survey weights
. reg goodbaduni i.pid3 i.agecat  b2.gender_trinary i.education borninusa i.raceethn  b2.prcpower [aweight = WEIGHT_W], robust
{txt}(sum of wgt is 1,220.67092468909)

Linear regression                               Number of obs     = {res}     1,240
                                                {txt}F(19, 1220)       =  {res}     8.09
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1246
                                                {txt}Root MSE          =    {res}  .3654

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    goodbaduni{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      t{col 64}   P>|t|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2} .0856575{col 44}{space 2} .0391464{col 55}{space 1}    2.19{col 64}{space 3}0.029{col 72}{space 4} .0088559{col 85}{space 3} .1624592
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2} .1785926{col 44}{space 2} .0374149{col 55}{space 1}    4.77{col 64}{space 3}0.000{col 72}{space 4}  .105188{col 85}{space 3} .2519972
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2}-.0582259{col 44}{space 2} .0452422{col 55}{space 1}   -1.29{col 64}{space 3}0.198{col 72}{space 4} -.146987{col 85}{space 3} .0305353
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2} -.110937{col 44}{space 2}  .047178{col 55}{space 1}   -2.35{col 64}{space 3}0.019{col 72}{space 4} -.203496{col 85}{space 3}-.0183779
{txt}{space 26}65+  {c |}{col 32}{res}{space 2}-.1296323{col 44}{space 2} .0512138{col 55}{space 1}   -2.53{col 64}{space 3}0.011{col 72}{space 4}-.2301091{col 85}{space 3}-.0291554
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .0193277{col 44}{space 2} .0299507{col 55}{space 1}    0.65{col 64}{space 3}0.519{col 72}{space 4} -.039433{col 85}{space 3} .0780884
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2} .0870793{col 44}{space 2} .0518629{col 55}{space 1}    1.68{col 64}{space 3}0.093{col 72}{space 4}-.0146709{col 85}{space 3} .1888296
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2}-.0081773{col 44}{space 2} .0754865{col 55}{space 1}   -0.11{col 64}{space 3}0.914{col 72}{space 4}-.1562751{col 85}{space 3} .1399204
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2} .0332341{col 44}{space 2} .0768539{col 55}{space 1}    0.43{col 64}{space 3}0.666{col 72}{space 4}-.1175464{col 85}{space 3} .1840145
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} .0028143{col 44}{space 2} .0863929{col 55}{space 1}    0.03{col 64}{space 3}0.974{col 72}{space 4}-.1666809{col 85}{space 3} .1723094
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2} .0468392{col 44}{space 2} .0725446{col 55}{space 1}    0.65{col 64}{space 3}0.519{col 72}{space 4}-.0954868{col 85}{space 3} .1891652
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2}  .159916{col 44}{space 2} .0687894{col 55}{space 1}    2.32{col 64}{space 3}0.020{col 72}{space 4} .0249573{col 85}{space 3} .2948747
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2} .0181339{col 44}{space 2} .0698543{col 55}{space 1}    0.26{col 64}{space 3}0.795{col 72}{space 4} -.118914{col 85}{space 3} .1551819
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2}-.0115372{col 44}{space 2} .0542228{col 55}{space 1}   -0.21{col 64}{space 3}0.832{col 72}{space 4}-.1179174{col 85}{space 3} .0948431
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2} .0255894{col 44}{space 2} .0627539{col 55}{space 1}    0.41{col 64}{space 3}0.684{col 72}{space 4}-.0975281{col 85}{space 3} .1487068
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.0375717{col 44}{space 2} .1090743{col 55}{space 1}   -0.34{col 64}{space 3}0.731{col 72}{space 4}-.2515658{col 85}{space 3} .1764223
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2} .0446256{col 44}{space 2} .0592671{col 55}{space 1}    0.75{col 64}{space 3}0.452{col 72}{space 4} -.071651{col 85}{space 3} .1609023
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-.0682467{col 44}{space 2} .1412951{col 55}{space 1}   -0.48{col 64}{space 3}0.629{col 72}{space 4} -.345455{col 85}{space 3} .2089616
{txt}{space 30} {c |}
{space 22}prcpower {c |}
{space 17}Top priority  {c |}{col 32}{res}{space 2}-.1149729{col 44}{space 2} .0303777{col 55}{space 1}   -3.78{col 64}{space 3}0.000{col 72}{space 4}-.1745713{col 85}{space 3}-.0553746
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .7928217{col 44}{space 2} .1091795{col 55}{space 1}    7.26{col 64}{space 3}0.000{col 72}{space 4} .5786213{col 85}{space 3} 1.007022
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est sto m2_weight
{txt}
{com}. local m2_N = e(N)
{txt}
{com}. 
. // Model 3a: Include China net thermometer rating 
. reg goodbaduni i.pid3 i.agecat  b2.gender_trinary i.education borninusa i.raceethn  china_net_mean if thermchi <= 100, robust

{txt}Linear regression                               Number of obs     = {res}     1,262
                                                {txt}F(19, 1242)       =  {res}    11.38
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1458
                                                {txt}Root MSE          =    {res} .36087

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    goodbaduni{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      t{col 64}   P>|t|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2} .1430179{col 44}{space 2} .0313439{col 55}{space 1}    4.56{col 64}{space 3}0.000{col 72}{space 4} .0815251{col 85}{space 3} .2045108
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2} .2260799{col 44}{space 2}  .028717{col 55}{space 1}    7.87{col 64}{space 3}0.000{col 72}{space 4} .1697406{col 85}{space 3} .2824191
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2}-.0531887{col 44}{space 2} .0259892{col 55}{space 1}   -2.05{col 64}{space 3}0.041{col 72}{space 4}-.1041762{col 85}{space 3}-.0022011
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}-.1187813{col 44}{space 2} .0302592{col 55}{space 1}   -3.93{col 64}{space 3}0.000{col 72}{space 4} -.178146{col 85}{space 3}-.0594166
{txt}{space 26}65+  {c |}{col 32}{res}{space 2} -.115261{col 44}{space 2} .0307795{col 55}{space 1}   -3.74{col 64}{space 3}0.000{col 72}{space 4}-.1756465{col 85}{space 3}-.0548756
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .0029467{col 44}{space 2} .0209438{col 55}{space 1}    0.14{col 64}{space 3}0.888{col 72}{space 4}-.0381425{col 85}{space 3} .0440359
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2}-.0242322{col 44}{space 2} .1010743{col 55}{space 1}   -0.24{col 64}{space 3}0.811{col 72}{space 4}-.2225274{col 85}{space 3} .1740631
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2} .0852296{col 44}{space 2} .0632124{col 55}{space 1}    1.35{col 64}{space 3}0.178{col 72}{space 4}-.0387853{col 85}{space 3} .2092444
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2} .1312698{col 44}{space 2} .0639352{col 55}{space 1}    2.05{col 64}{space 3}0.040{col 72}{space 4} .0058369{col 85}{space 3} .2567028
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} .1980434{col 44}{space 2}  .069567{col 55}{space 1}    2.85{col 64}{space 3}0.004{col 72}{space 4} .0615616{col 85}{space 3} .3345252
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2} .1920601{col 44}{space 2} .0614238{col 55}{space 1}    3.13{col 64}{space 3}0.002{col 72}{space 4} .0715542{col 85}{space 3}  .312566
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2} .2545733{col 44}{space 2} .0609748{col 55}{space 1}    4.18{col 64}{space 3}0.000{col 72}{space 4} .1349483{col 85}{space 3} .3741983
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2} -.096134{col 44}{space 2} .0280589{col 55}{space 1}   -3.43{col 64}{space 3}0.001{col 72}{space 4}-.1511821{col 85}{space 3}-.0410859
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2}-.0351101{col 44}{space 2} .0369635{col 55}{space 1}   -0.95{col 64}{space 3}0.342{col 72}{space 4} -.107628{col 85}{space 3} .0374078
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2}-.0146141{col 44}{space 2} .0320954{col 55}{space 1}   -0.46{col 64}{space 3}0.649{col 72}{space 4}-.0775813{col 85}{space 3}  .048353
{txt}{space 24}Other  {c |}{col 32}{res}{space 2} -.017362{col 44}{space 2} .0577497{col 55}{space 1}   -0.30{col 64}{space 3}0.764{col 72}{space 4}-.1306597{col 85}{space 3} .0959357
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2}-.0359266{col 44}{space 2} .0343283{col 55}{space 1}   -1.05{col 64}{space 3}0.296{col 72}{space 4}-.1032745{col 85}{space 3} .0314213
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2} -.176179{col 44}{space 2} .1887689{col 55}{space 1}   -0.93{col 64}{space 3}0.351{col 72}{space 4}-.5465201{col 85}{space 3} .1941621
{txt}{space 30} {c |}
{space 16}china_net_mean {c |}{col 32}{res}{space 2} .0015479{col 44}{space 2}  .000631{col 55}{space 1}    2.45{col 64}{space 3}0.014{col 72}{space 4} .0003099{col 85}{space 3} .0027859
{txt}{space 25}_cons {c |}{col 32}{res}{space 2} .7134213{col 44}{space 2} .0756302{col 55}{space 1}    9.43{col 64}{space 3}0.000{col 72}{space 4} .5650442{col 85}{space 3} .8617984
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est sto m3a
{txt}
{com}. 
. // Model 3b: Include US-China relationship characterization
. reg goodbaduni i.pid3 i.agecat  b2.gender_trinary i.education borninusa i.raceethn  i.frenemy, robust

{txt}Linear regression                               Number of obs     = {res}     2,500
                                                {txt}F(20, 2479)       =  {res}    22.13
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1599
                                                {txt}Root MSE          =    {res} .35208

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    goodbaduni{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      t{col 64}   P>|t|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2} .0897268{col 44}{space 2} .0215645{col 55}{space 1}    4.16{col 64}{space 3}0.000{col 72}{space 4} .0474405{col 85}{space 3} .1320132
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2} .1533283{col 44}{space 2} .0204788{col 55}{space 1}    7.49{col 64}{space 3}0.000{col 72}{space 4} .1131709{col 85}{space 3} .1934856
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2}-.0433796{col 44}{space 2}  .018538{col 55}{space 1}   -2.34{col 64}{space 3}0.019{col 72}{space 4}-.0797311{col 85}{space 3}-.0070281
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}-.0929486{col 44}{space 2} .0210912{col 55}{space 1}   -4.41{col 64}{space 3}0.000{col 72}{space 4}-.1343068{col 85}{space 3}-.0515904
{txt}{space 26}65+  {c |}{col 32}{res}{space 2}-.1002003{col 44}{space 2} .0218741{col 55}{space 1}   -4.58{col 64}{space 3}0.000{col 72}{space 4}-.1430937{col 85}{space 3}-.0573068
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .0086591{col 44}{space 2} .0145817{col 55}{space 1}    0.59{col 64}{space 3}0.553{col 72}{space 4}-.0199344{col 85}{space 3} .0372526
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2}-.0026236{col 44}{space 2} .0576593{col 55}{space 1}   -0.05{col 64}{space 3}0.964{col 72}{space 4} -.115689{col 85}{space 3} .1104418
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2} .0494163{col 44}{space 2} .0407361{col 55}{space 1}    1.21{col 64}{space 3}0.225{col 72}{space 4} -.030464{col 85}{space 3} .1292966
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2} .1032135{col 44}{space 2} .0411386{col 55}{space 1}    2.51{col 64}{space 3}0.012{col 72}{space 4} .0225439{col 85}{space 3}  .183883
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} .1293675{col 44}{space 2} .0461258{col 55}{space 1}    2.80{col 64}{space 3}0.005{col 72}{space 4} .0389184{col 85}{space 3} .2198166
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2} .1393659{col 44}{space 2} .0395284{col 55}{space 1}    3.53{col 64}{space 3}0.000{col 72}{space 4} .0618538{col 85}{space 3} .2168779
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2} .2072324{col 44}{space 2} .0387485{col 55}{space 1}    5.35{col 64}{space 3}0.000{col 72}{space 4} .1312495{col 85}{space 3} .2832152
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2}-.0696274{col 44}{space 2} .0200925{col 55}{space 1}   -3.47{col 64}{space 3}0.001{col 72}{space 4}-.1090273{col 85}{space 3}-.0302276
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2}-.0481991{col 44}{space 2} .0260805{col 55}{space 1}   -1.85{col 64}{space 3}0.065{col 72}{space 4}-.0993409{col 85}{space 3} .0029426
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2}-.0039336{col 44}{space 2} .0217771{col 55}{space 1}   -0.18{col 64}{space 3}0.857{col 72}{space 4}-.0466367{col 85}{space 3} .0387696
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.0403936{col 44}{space 2} .0409005{col 55}{space 1}   -0.99{col 64}{space 3}0.323{col 72}{space 4}-.1205964{col 85}{space 3} .0398092
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2}-.0347456{col 44}{space 2} .0266399{col 55}{space 1}   -1.30{col 64}{space 3}0.192{col 72}{space 4}-.0869844{col 85}{space 3} .0174931
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-.0572479{col 44}{space 2} .1073539{col 55}{space 1}   -0.53{col 64}{space 3}0.594{col 72}{space 4}-.2677605{col 85}{space 3} .1532646
{txt}{space 30} {c |}
{space 23}frenemy {c |}
{space 19}Competitor  {c |}{col 32}{res}{space 2} -.008128{col 44}{space 2} .0198805{col 55}{space 1}   -0.41{col 64}{space 3}0.683{col 72}{space 4}-.0471122{col 85}{space 3} .0308562
{txt}{space 24}Enemy  {c |}{col 32}{res}{space 2}-.1729152{col 44}{space 2} .0244836{col 55}{space 1}   -7.06{col 64}{space 3}0.000{col 72}{space 4}-.2209257{col 85}{space 3}-.1249047
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} .8181819{col 44}{space 2} .0517097{col 55}{space 1}   15.82{col 64}{space 3}0.000{col 72}{space 4} .7167833{col 85}{space 3} .9195805
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est sto m3b
{txt}
{com}. 
. * * * * Logit for robustness
. 
. // Model 1L: Base logistic model predicting support for international students
. logit goodbaduni i.pid3 i.agecat b2.gender_trinary i.education borninusa i.raceethn, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-1175.5296}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-1025.2756}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-1006.1322}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-1005.8221}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-1005.8215}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-1005.8215}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:2,516}
{txt}{col 57}{lalign 13:Wald chi2({res:18})}{col 70} = {res}{ralign 6:284.73}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1005.8215}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1444}

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    goodbaduni{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      z{col 64}   P>|z|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2} .7126341{col 44}{space 2} .1306838{col 55}{space 1}    5.45{col 64}{space 3}0.000{col 72}{space 4} .4564985{col 85}{space 3} .9687697
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2} 1.649426{col 44}{space 2} .1648979{col 55}{space 1}   10.00{col 64}{space 3}0.000{col 72}{space 4} 1.326232{col 85}{space 3}  1.97262
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2}-.6971927{col 44}{space 2} .2707293{col 55}{space 1}   -2.58{col 64}{space 3}0.010{col 72}{space 4}-1.227812{col 85}{space 3} -.166573
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}-1.244358{col 44}{space 2} .2682582{col 55}{space 1}   -4.64{col 64}{space 3}0.000{col 72}{space 4}-1.770134{col 85}{space 3}-.7185816
{txt}{space 26}65+  {c |}{col 32}{res}{space 2}-1.374754{col 44}{space 2} .2721201{col 55}{space 1}   -5.05{col 64}{space 3}0.000{col 72}{space 4}-1.908099{col 85}{space 3}-.8414081
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .0291004{col 44}{space 2} .1160071{col 55}{space 1}    0.25{col 64}{space 3}0.802{col 72}{space 4}-.1982693{col 85}{space 3} .2564701
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2} .1101212{col 44}{space 2} .6142335{col 55}{space 1}    0.18{col 64}{space 3}0.858{col 72}{space 4}-1.093754{col 85}{space 3} 1.313997
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2} .3191787{col 44}{space 2}  .230967{col 55}{space 1}    1.38{col 64}{space 3}0.167{col 72}{space 4}-.1335082{col 85}{space 3} .7718656
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2}  .624346{col 44}{space 2} .2479192{col 55}{space 1}    2.52{col 64}{space 3}0.012{col 72}{space 4} .1384332{col 85}{space 3} 1.110259
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} .7720344{col 44}{space 2} .2875148{col 55}{space 1}    2.69{col 64}{space 3}0.007{col 72}{space 4} .2085157{col 85}{space 3} 1.335553
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2} .9005083{col 44}{space 2}  .245368{col 55}{space 1}    3.67{col 64}{space 3}0.000{col 72}{space 4} .4195958{col 85}{space 3} 1.381421
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2} 1.935629{col 44}{space 2} .2976038{col 55}{space 1}    6.50{col 64}{space 3}0.000{col 72}{space 4} 1.352336{col 85}{space 3} 2.518922
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2}-.8584067{col 44}{space 2} .2563783{col 55}{space 1}   -3.35{col 64}{space 3}0.001{col 72}{space 4}-1.360899{col 85}{space 3}-.3559144
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2}-.3914822{col 44}{space 2} .2297558{col 55}{space 1}   -1.70{col 64}{space 3}0.088{col 72}{space 4}-.8417953{col 85}{space 3} .0588309
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2}-.1087572{col 44}{space 2}  .220249{col 55}{space 1}   -0.49{col 64}{space 3}0.621{col 72}{space 4}-.5404373{col 85}{space 3} .3229229
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}  -.23068{col 44}{space 2} .3099098{col 55}{space 1}   -0.74{col 64}{space 3}0.457{col 72}{space 4}-.8380919{col 85}{space 3}  .376732
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2}-.3053018{col 44}{space 2} .3675415{col 55}{space 1}   -0.83{col 64}{space 3}0.406{col 72}{space 4} -1.02567{col 85}{space 3} .4150663
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-.6754409{col 44}{space 2} .6686807{col 55}{space 1}   -1.01{col 64}{space 3}0.312{col 72}{space 4}-1.986031{col 85}{space 3} .6351493
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 1.963765{col 44}{space 2} .4355998{col 55}{space 1}    4.51{col 64}{space 3}0.000{col 72}{space 4} 1.110005{col 85}{space 3} 2.817525
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto m1L
{txt}
{com}. local m1L_N = e(N)
{txt}
{com}. 
. // Model 2L: Add China power containment priority variable
. logit goodbaduni i.pid3 i.agecat b2.gender_trinary i.education borninusa i.raceethn b2.prcpower, robust

{txt}note: {bf:3.gender_trinary} != 0 predicts success perfectly;
      {bf:3.gender_trinary} omitted and 10 obs not used.

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-566.88038}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-490.94882}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-480.20237}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-479.95545}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-479.95539}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-479.95539}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,230}
{txt}{col 57}{lalign 13:Wald chi2({res:18})}{col 70} = {res}{ralign 6:140.31}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-479.95539}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1533}

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    goodbaduni{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      z{col 64}   P>|z|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2}  .472071{col 44}{space 2} .1898316{col 55}{space 1}    2.49{col 64}{space 3}0.013{col 72}{space 4} .1000079{col 85}{space 3}  .844134
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2} 1.313931{col 44}{space 2} .2498771{col 55}{space 1}    5.26{col 64}{space 3}0.000{col 72}{space 4}  .824181{col 85}{space 3} 1.803681
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2}-.6334791{col 44}{space 2} .4127175{col 55}{space 1}   -1.53{col 64}{space 3}0.125{col 72}{space 4} -1.44239{col 85}{space 3} .1754323
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}-1.042266{col 44}{space 2} .4100527{col 55}{space 1}   -2.54{col 64}{space 3}0.011{col 72}{space 4}-1.845955{col 85}{space 3}-.2385778
{txt}{space 26}65+  {c |}{col 32}{res}{space 2}-1.213368{col 44}{space 2} .4193023{col 55}{space 1}   -2.89{col 64}{space 3}0.004{col 72}{space 4}-2.035186{col 85}{space 3} -.391551
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .1330976{col 44}{space 2} .1677147{col 55}{space 1}    0.79{col 64}{space 3}0.427{col 72}{space 4}-.1956172{col 85}{space 3} .4618124
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2}        0{col 44}{txt}  (empty)
{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2} .1312916{col 44}{space 2} .3305265{col 55}{space 1}    0.40{col 64}{space 3}0.691{col 72}{space 4}-.5165284{col 85}{space 3} .7791117
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2} .4927813{col 44}{space 2} .3556452{col 55}{space 1}    1.39{col 64}{space 3}0.166{col 72}{space 4}-.2042704{col 85}{space 3} 1.189833
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} .3496916{col 44}{space 2} .3988573{col 55}{space 1}    0.88{col 64}{space 3}0.381{col 72}{space 4}-.4320543{col 85}{space 3} 1.131437
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2} .5981156{col 44}{space 2}  .351458{col 55}{space 1}    1.70{col 64}{space 3}0.089{col 72}{space 4}-.0907294{col 85}{space 3} 1.286961
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2} 2.006729{col 44}{space 2} .4692004{col 55}{space 1}    4.28{col 64}{space 3}0.000{col 72}{space 4} 1.087113{col 85}{space 3} 2.926345
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2}-.6210449{col 44}{space 2} .3880317{col 55}{space 1}   -1.60{col 64}{space 3}0.109{col 72}{space 4}-1.381573{col 85}{space 3} .1394833
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2}-.4828099{col 44}{space 2} .3357435{col 55}{space 1}   -1.44{col 64}{space 3}0.150{col 72}{space 4}-1.140855{col 85}{space 3} .1752351
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2} .1221433{col 44}{space 2} .3276687{col 55}{space 1}    0.37{col 64}{space 3}0.709{col 72}{space 4}-.5200755{col 85}{space 3} .7643621
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.2156622{col 44}{space 2}  .436764{col 55}{space 1}   -0.49{col 64}{space 3}0.621{col 72}{space 4}-1.071704{col 85}{space 3} .6403794
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2}-.5840697{col 44}{space 2} .5472982{col 55}{space 1}   -1.07{col 64}{space 3}0.286{col 72}{space 4}-1.656754{col 85}{space 3} .4886151
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-.3208948{col 44}{space 2} 1.149491{col 55}{space 1}   -0.28{col 64}{space 3}0.780{col 72}{space 4}-2.573856{col 85}{space 3} 1.932066
{txt}{space 30} {c |}
{space 22}prcpower {c |}
{space 17}Top priority  {c |}{col 32}{res}{space 2} -.824514{col 44}{space 2} .1828175{col 55}{space 1}   -4.51{col 64}{space 3}0.000{col 72}{space 4} -1.18283{col 85}{space 3}-.4661984
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 2.476109{col 44}{space 2} .6656824{col 55}{space 1}    3.72{col 64}{space 3}0.000{col 72}{space 4} 1.171396{col 85}{space 3} 3.780823
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto m2L
{txt}
{com}. local m2L_N = e(N)
{txt}
{com}. 
. // Model 3aL: Include China net thermometer rating 
. logit goodbaduni i.pid3 i.agecat b2.gender_trinary i.education borninusa i.raceethn china_net_mean if thermchi <= 100, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-602.17517}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-513.59587}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-502.43261}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-502.27611}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-502.27593}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-502.27593}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,262}
{txt}{col 57}{lalign 13:Wald chi2({res:19})}{col 70} = {res}{ralign 6:177.73}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-502.27593}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1659}

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    goodbaduni{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      z{col 64}   P>|z|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2} .7804648{col 44}{space 2} .1901846{col 55}{space 1}    4.10{col 64}{space 3}0.000{col 72}{space 4} .4077099{col 85}{space 3}  1.15322
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2} 1.703289{col 44}{space 2} .2300771{col 55}{space 1}    7.40{col 64}{space 3}0.000{col 72}{space 4} 1.252346{col 85}{space 3} 2.154231
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2}-.6877365{col 44}{space 2} .3603607{col 55}{space 1}   -1.91{col 64}{space 3}0.056{col 72}{space 4} -1.39403{col 85}{space 3} .0185575
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}-1.256115{col 44}{space 2} .3534805{col 55}{space 1}   -3.55{col 64}{space 3}0.000{col 72}{space 4}-1.948924{col 85}{space 3}-.5633062
{txt}{space 26}65+  {c |}{col 32}{res}{space 2}-1.212834{col 44}{space 2}   .35685{col 55}{space 1}   -3.40{col 64}{space 3}0.001{col 72}{space 4}-1.912247{col 85}{space 3}-.5134209
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .0171774{col 44}{space 2} .1646014{col 55}{space 1}    0.10{col 64}{space 3}0.917{col 72}{space 4}-.3054354{col 85}{space 3} .3397901
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2}-.1780143{col 44}{space 2} .7294524{col 55}{space 1}   -0.24{col 64}{space 3}0.807{col 72}{space 4}-1.607715{col 85}{space 3} 1.251686
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2} .5737384{col 44}{space 2} .3378309{col 55}{space 1}    1.70{col 64}{space 3}0.089{col 72}{space 4}-.0883981{col 85}{space 3} 1.235875
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2} .8034635{col 44}{space 2}  .357056{col 55}{space 1}    2.25{col 64}{space 3}0.024{col 72}{space 4} .1036465{col 85}{space 3}  1.50328
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} 1.331841{col 44}{space 2} .4312456{col 55}{space 1}    3.09{col 64}{space 3}0.002{col 72}{space 4} .4866147{col 85}{space 3} 2.177067
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2} 1.288641{col 44}{space 2} .3557787{col 55}{space 1}    3.62{col 64}{space 3}0.000{col 72}{space 4} .5913271{col 85}{space 3} 1.985954
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2} 2.077705{col 44}{space 2} .4107917{col 55}{space 1}    5.06{col 64}{space 3}0.000{col 72}{space 4} 1.272568{col 85}{space 3} 2.882842
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2}-1.054839{col 44}{space 2} .3430584{col 55}{space 1}   -3.07{col 64}{space 3}0.002{col 72}{space 4}-1.727221{col 85}{space 3} -.382457
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2}-.3824342{col 44}{space 2} .3261852{col 55}{space 1}   -1.17{col 64}{space 3}0.241{col 72}{space 4}-1.021745{col 85}{space 3} .2568771
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2}-.2496592{col 44}{space 2} .2989178{col 55}{space 1}   -0.84{col 64}{space 3}0.404{col 72}{space 4}-.8355272{col 85}{space 3} .3362088
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.2234175{col 44}{space 2} .4390206{col 55}{space 1}   -0.51{col 64}{space 3}0.611{col 72}{space 4}-1.083882{col 85}{space 3}  .637047
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2}-.2567572{col 44}{space 2} .4860492{col 55}{space 1}   -0.53{col 64}{space 3}0.597{col 72}{space 4}-1.209396{col 85}{space 3} .6958817
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-1.086536{col 44}{space 2} 1.084535{col 55}{space 1}   -1.00{col 64}{space 3}0.316{col 72}{space 4}-3.212185{col 85}{space 3} 1.039113
{txt}{space 30} {c |}
{space 16}china_net_mean {c |}{col 32}{res}{space 2} .0115311{col 44}{space 2} .0050084{col 55}{space 1}    2.30{col 64}{space 3}0.021{col 72}{space 4} .0017148{col 85}{space 3} .0213475
{txt}{space 25}_cons {c |}{col 32}{res}{space 2} 1.892579{col 44}{space 2} .6007733{col 55}{space 1}    3.15{col 64}{space 3}0.002{col 72}{space 4} .7150846{col 85}{space 3} 3.070073
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto m3aL
{txt}
{com}. 
. // Model 3bL: Include US-China relationship characterization
. logit goodbaduni i.pid3 i.agecat b2.gender_trinary i.education borninusa i.raceethn i.frenemy, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -1170.868}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-982.07902}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-956.06626}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-955.51605}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-955.51444}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-955.51444}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:2,500}
{txt}{col 57}{lalign 13:Wald chi2({res:20})}{col 70} = {res}{ralign 6:350.01}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-955.51444}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1839}

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    goodbaduni{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      z{col 64}   P>|z|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2} .4724974{col 44}{space 2} .1383079{col 55}{space 1}    3.42{col 64}{space 3}0.001{col 72}{space 4}  .201419{col 85}{space 3} .7435759
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2} 1.289161{col 44}{space 2} .1751851{col 55}{space 1}    7.36{col 64}{space 3}0.000{col 72}{space 4} .9458045{col 85}{space 3} 1.632518
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2}-.5995627{col 44}{space 2} .2726704{col 55}{space 1}   -2.20{col 64}{space 3}0.028{col 72}{space 4}-1.133987{col 85}{space 3}-.0651385
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}-1.050959{col 44}{space 2} .2668435{col 55}{space 1}   -3.94{col 64}{space 3}0.000{col 72}{space 4}-1.573963{col 85}{space 3}-.5279557
{txt}{space 26}65+  {c |}{col 32}{res}{space 2} -1.08592{col 44}{space 2} .2701033{col 55}{space 1}   -4.02{col 64}{space 3}0.000{col 72}{space 4}-1.615313{col 85}{space 3}-.5565273
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .0746935{col 44}{space 2} .1197157{col 55}{space 1}    0.62{col 64}{space 3}0.533{col 72}{space 4} -.159945{col 85}{space 3}  .309332
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2}-.0440795{col 44}{space 2} .5839188{col 55}{space 1}   -0.08{col 64}{space 3}0.940{col 72}{space 4}-1.188539{col 85}{space 3}  1.10038
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2}   .34997{col 44}{space 2} .2360741{col 55}{space 1}    1.48{col 64}{space 3}0.138{col 72}{space 4}-.1127267{col 85}{space 3} .8126668
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2} .6617257{col 44}{space 2} .2544928{col 55}{space 1}    2.60{col 64}{space 3}0.009{col 72}{space 4}  .162929{col 85}{space 3} 1.160522
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} .8901502{col 44}{space 2} .2934145{col 55}{space 1}    3.03{col 64}{space 3}0.002{col 72}{space 4} .3150684{col 85}{space 3} 1.465232
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2} .9311392{col 44}{space 2} .2501641{col 55}{space 1}    3.72{col 64}{space 3}0.000{col 72}{space 4} .4408265{col 85}{space 3} 1.421452
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2} 1.960276{col 44}{space 2} .3059944{col 55}{space 1}    6.41{col 64}{space 3}0.000{col 72}{space 4} 1.360538{col 85}{space 3} 2.560014
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2}-.7390307{col 44}{space 2} .2622498{col 55}{space 1}   -2.82{col 64}{space 3}0.005{col 72}{space 4}-1.253031{col 85}{space 3}-.2250306
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2}-.6125065{col 44}{space 2} .2332211{col 55}{space 1}   -2.63{col 64}{space 3}0.009{col 72}{space 4}-1.069612{col 85}{space 3}-.1554014
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2}-.0680512{col 44}{space 2} .2246052{col 55}{space 1}   -0.30{col 64}{space 3}0.762{col 72}{space 4}-.5082692{col 85}{space 3} .3721668
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.3629253{col 44}{space 2}  .310481{col 55}{space 1}   -1.17{col 64}{space 3}0.242{col 72}{space 4} -.971457{col 85}{space 3} .2456063
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2}-.3005972{col 44}{space 2} .3737129{col 55}{space 1}   -0.80{col 64}{space 3}0.421{col 72}{space 4}-1.033061{col 85}{space 3} .4318665
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-.3471933{col 44}{space 2} .7044838{col 55}{space 1}   -0.49{col 64}{space 3}0.622{col 72}{space 4}-1.727956{col 85}{space 3}  1.03357
{txt}{space 30} {c |}
{space 23}frenemy {c |}
{space 19}Competitor  {c |}{col 32}{res}{space 2}-.1269652{col 44}{space 2} .2953616{col 55}{space 1}   -0.43{col 64}{space 3}0.667{col 72}{space 4}-.7058632{col 85}{space 3} .4519329
{txt}{space 24}Enemy  {c |}{col 32}{res}{space 2}-1.281661{col 44}{space 2} .2954488{col 55}{space 1}   -4.34{col 64}{space 3}0.000{col 72}{space 4} -1.86073{col 85}{space 3}-.7025925
{txt}{space 30} {c |}
{space 25}_cons {c |}{col 32}{res}{space 2} 2.491492{col 44}{space 2} .5241424{col 55}{space 1}    4.75{col 64}{space 3}0.000{col 72}{space 4} 1.464192{col 85}{space 3} 3.518792
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto m3bL
{txt}
{com}. 
. 
. /*
> Not displayed
> 
> esttab m1 m1L m3a m3aL m3b m3bL, lab star(+ 0.10 * 0.05) nobase noomit 
> 
> 
> */
. 
. * * * * * * Support / Oppose Limiting Chinese Students (1 through 4)
.         
.         
. ologit supportopposeprc i.pid3 i.agecat  b2.gender_trinary i.education borninusa i.raceethn , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3389.8123}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3119.4791}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3115.2586}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-3115.2448}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-3115.2448}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:2,509}
{txt}{col 57}{lalign 13:Wald chi2({res:18})}{col 70} = {res}{ralign 6:488.00}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3115.2448}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0810}

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}              supportopposeprc{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      z{col 64}   P>|z|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2}-.8728785{col 44}{space 2} .0984858{col 55}{space 1}   -8.86{col 64}{space 3}0.000{col 72}{space 4}-1.065907{col 85}{space 3}-.6798499
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2}-1.460418{col 44}{space 2} .1016422{col 55}{space 1}  -14.37{col 64}{space 3}0.000{col 72}{space 4}-1.659633{col 85}{space 3}-1.261203
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2} .6169712{col 44}{space 2} .1300373{col 55}{space 1}    4.74{col 64}{space 3}0.000{col 72}{space 4} .3621027{col 85}{space 3} .8718396
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2} 1.441589{col 44}{space 2} .1358109{col 55}{space 1}   10.61{col 64}{space 3}0.000{col 72}{space 4} 1.175404{col 85}{space 3} 1.707773
{txt}{space 26}65+  {c |}{col 32}{res}{space 2} 1.579828{col 44}{space 2} .1378418{col 55}{space 1}   11.46{col 64}{space 3}0.000{col 72}{space 4} 1.309663{col 85}{space 3} 1.849993
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .1633954{col 44}{space 2} .0758282{col 55}{space 1}    2.15{col 64}{space 3}0.031{col 72}{space 4} .0147749{col 85}{space 3} .3120159
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2}   .09548{col 44}{space 2}  .389445{col 55}{space 1}    0.25{col 64}{space 3}0.806{col 72}{space 4}-.6678181{col 85}{space 3} .8587782
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2} -.261848{col 44}{space 2} .2035115{col 55}{space 1}   -1.29{col 64}{space 3}0.198{col 72}{space 4}-.6607232{col 85}{space 3} .1370271
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2}-.3137893{col 44}{space 2} .2082292{col 55}{space 1}   -1.51{col 64}{space 3}0.132{col 72}{space 4}-.7219111{col 85}{space 3} .0943324
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} -.458067{col 44}{space 2} .2314083{col 55}{space 1}   -1.98{col 64}{space 3}0.048{col 72}{space 4}-.9116189{col 85}{space 3} -.004515
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2}-.4495397{col 44}{space 2}  .204245{col 55}{space 1}   -2.20{col 64}{space 3}0.028{col 72}{space 4}-.8498526{col 85}{space 3}-.0492268
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2}-.8751212{col 44}{space 2} .2077253{col 55}{space 1}   -4.21{col 64}{space 3}0.000{col 72}{space 4}-1.282255{col 85}{space 3}-.4679871
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2} .2339305{col 44}{space 2} .1299912{col 55}{space 1}    1.80{col 64}{space 3}0.072{col 72}{space 4}-.0208476{col 85}{space 3} .4887086
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2} .2703077{col 44}{space 2}  .135566{col 55}{space 1}    1.99{col 64}{space 3}0.046{col 72}{space 4} .0046032{col 85}{space 3} .5360122
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2} .2672823{col 44}{space 2} .1266652{col 55}{space 1}    2.11{col 64}{space 3}0.035{col 72}{space 4}  .019023{col 85}{space 3} .5155416
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.1616566{col 44}{space 2} .2207253{col 55}{space 1}   -0.73{col 64}{space 3}0.464{col 72}{space 4}-.5942702{col 85}{space 3}  .270957
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2} .3270423{col 44}{space 2} .2090653{col 55}{space 1}    1.56{col 64}{space 3}0.118{col 72}{space 4}-.0827182{col 85}{space 3} .7368028
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2} -.128531{col 44}{space 2} .5403593{col 55}{space 1}   -0.24{col 64}{space 3}0.812{col 72}{space 4}-1.187616{col 85}{space 3} .9305537
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}/cut1 {c |}{col 32}{res}{space 2}-1.720464{col 44}{space 2} .2599186{col 72}{space 4}-2.229895{col 85}{space 3}-1.211033
{txt}{space 25}/cut2 {c |}{col 32}{res}{space 2} -.229686{col 44}{space 2} .2587628{col 72}{space 4}-.7368517{col 85}{space 3} .2774798
{txt}{space 25}/cut3 {c |}{col 32}{res}{space 2} 1.595302{col 44}{space 2} .2600379{col 72}{space 4} 1.085637{col 85}{space 3} 2.104967
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto m4
{txt}
{com}. local m4_N = e(N)
{txt}
{com}. 
. 
. ologit supportopposeprc i.pid3 i.agecat  b2.gender_trinary i.education borninusa i.raceethn b2.prcpower, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-1672.4011}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-1504.3933}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-1500.8828}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-1500.8746}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-1500.8746}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,241}
{txt}{col 57}{lalign 13:Wald chi2({res:19})}{col 70} = {res}{ralign 6:308.96}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1500.8746}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1026}

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}              supportopposeprc{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      z{col 64}   P>|z|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2}-.6841998{col 44}{space 2} .1382195{col 55}{space 1}   -4.95{col 64}{space 3}0.000{col 72}{space 4}-.9551051{col 85}{space 3}-.4132945
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2}-1.244132{col 44}{space 2} .1526895{col 55}{space 1}   -8.15{col 64}{space 3}0.000{col 72}{space 4}-1.543398{col 85}{space 3}-.9448662
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2} .4684548{col 44}{space 2} .1925422{col 55}{space 1}    2.43{col 64}{space 3}0.015{col 72}{space 4} .0910789{col 85}{space 3} .8458306
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}  1.17862{col 44}{space 2} .1997481{col 55}{space 1}    5.90{col 64}{space 3}0.000{col 72}{space 4} .7871206{col 85}{space 3} 1.570119
{txt}{space 26}65+  {c |}{col 32}{res}{space 2} 1.350521{col 44}{space 2} .2029749{col 55}{space 1}    6.65{col 64}{space 3}0.000{col 72}{space 4} .9526972{col 85}{space 3} 1.748344
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .1036671{col 44}{space 2} .1088727{col 55}{space 1}    0.95{col 64}{space 3}0.341{col 72}{space 4}-.1097194{col 85}{space 3} .3170536
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2} .7945658{col 44}{space 2} .7234718{col 55}{space 1}    1.10{col 64}{space 3}0.272{col 72}{space 4}-.6234128{col 85}{space 3} 2.212544
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2}-.1011304{col 44}{space 2} .3179635{col 55}{space 1}   -0.32{col 64}{space 3}0.750{col 72}{space 4}-.7243274{col 85}{space 3} .5220666
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2}-.2585115{col 44}{space 2} .3228797{col 55}{space 1}   -0.80{col 64}{space 3}0.423{col 72}{space 4}-.8913441{col 85}{space 3} .3743211
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2}  -.26247{col 44}{space 2} .3392158{col 55}{space 1}   -0.77{col 64}{space 3}0.439{col 72}{space 4}-.9273208{col 85}{space 3} .4023808
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2}-.2629094{col 44}{space 2} .3154387{col 55}{space 1}   -0.83{col 64}{space 3}0.405{col 72}{space 4}-.8811578{col 85}{space 3} .3553391
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2}-.6693261{col 44}{space 2} .3211571{col 55}{space 1}   -2.08{col 64}{space 3}0.037{col 72}{space 4}-1.298782{col 85}{space 3}-.0398699
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2} .2323578{col 44}{space 2} .1914102{col 55}{space 1}    1.21{col 64}{space 3}0.225{col 72}{space 4}-.1427992{col 85}{space 3} .6075149
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2} .4512908{col 44}{space 2}  .209997{col 55}{space 1}    2.15{col 64}{space 3}0.032{col 72}{space 4} .0397043{col 85}{space 3} .8628774
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2} .1706812{col 44}{space 2} .1784068{col 55}{space 1}    0.96{col 64}{space 3}0.339{col 72}{space 4}-.1789897{col 85}{space 3} .5203522
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.0891655{col 44}{space 2} .3061595{col 55}{space 1}   -0.29{col 64}{space 3}0.771{col 72}{space 4}-.6892271{col 85}{space 3} .5108962
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2} .4118025{col 44}{space 2} .2984926{col 55}{space 1}    1.38{col 64}{space 3}0.168{col 72}{space 4}-.1732322{col 85}{space 3} .9968371
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-.7867755{col 44}{space 2} 1.088527{col 55}{space 1}   -0.72{col 64}{space 3}0.470{col 72}{space 4}-2.920249{col 85}{space 3} 1.346698
{txt}{space 30} {c |}
{space 22}prcpower {c |}
{space 17}Top priority  {c |}{col 32}{res}{space 2} 1.005514{col 44}{space 2} .1144631{col 55}{space 1}    8.78{col 64}{space 3}0.000{col 72}{space 4} .7811704{col 85}{space 3} 1.229858
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}/cut1 {c |}{col 32}{res}{space 2}-1.217061{col 44}{space 2} .3850915{col 72}{space 4}-1.971826{col 85}{space 3}-.4622953
{txt}{space 25}/cut2 {c |}{col 32}{res}{space 2} .3803966{col 44}{space 2}  .381635{col 72}{space 4}-.3675943{col 85}{space 3} 1.128387
{txt}{space 25}/cut3 {c |}{col 32}{res}{space 2} 2.301601{col 44}{space 2} .3881199{col 72}{space 4}   1.5409{col 85}{space 3} 3.062302
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto m5
{txt}
{com}. local m5_N = e(N)
{txt}
{com}. 
. 
. ologit supportopposeprc i.pid3 i.agecat  b2.gender_trinary i.education borninusa i.raceethn china_net_mean if thermchi < 100, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-1677.9988}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-1507.6672}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-1504.0424}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-1504.0356}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-1504.0356}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,241}
{txt}{col 57}{lalign 13:Wald chi2({res:19})}{col 70} = {res}{ralign 6:282.88}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1504.0356}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1037}

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}              supportopposeprc{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      z{col 64}   P>|z|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2}-.7763518{col 44}{space 2} .1446277{col 55}{space 1}   -5.37{col 64}{space 3}0.000{col 72}{space 4}-1.059817{col 85}{space 3}-.4928867
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2}-1.315058{col 44}{space 2} .1470463{col 55}{space 1}   -8.94{col 64}{space 3}0.000{col 72}{space 4}-1.603263{col 85}{space 3}-1.026852
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2} .6196584{col 44}{space 2} .1826801{col 55}{space 1}    3.39{col 64}{space 3}0.001{col 72}{space 4} .2616119{col 85}{space 3} .9777049
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2} 1.392363{col 44}{space 2} .1923319{col 55}{space 1}    7.24{col 64}{space 3}0.000{col 72}{space 4} 1.015399{col 85}{space 3} 1.769326
{txt}{space 26}65+  {c |}{col 32}{res}{space 2} 1.389109{col 44}{space 2} .1974369{col 55}{space 1}    7.04{col 64}{space 3}0.000{col 72}{space 4}  1.00214{col 85}{space 3} 1.776079
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .1058089{col 44}{space 2} .1083992{col 55}{space 1}    0.98{col 64}{space 3}0.329{col 72}{space 4}-.1066497{col 85}{space 3} .3182675
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2} -.625664{col 44}{space 2} .4396813{col 55}{space 1}   -1.42{col 64}{space 3}0.155{col 72}{space 4}-1.487424{col 85}{space 3} .2360956
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2}-.5489161{col 44}{space 2} .3057476{col 55}{space 1}   -1.80{col 64}{space 3}0.073{col 72}{space 4} -1.14817{col 85}{space 3} .0503382
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2} -.463231{col 44}{space 2} .3130235{col 55}{space 1}   -1.48{col 64}{space 3}0.139{col 72}{space 4}-1.076746{col 85}{space 3} .1502838
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2}-.7921604{col 44}{space 2} .3639494{col 55}{space 1}   -2.18{col 64}{space 3}0.030{col 72}{space 4}-1.505488{col 85}{space 3}-.0788326
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2}-.7651406{col 44}{space 2} .3063211{col 55}{space 1}   -2.50{col 64}{space 3}0.012{col 72}{space 4}-1.365519{col 85}{space 3}-.1647622
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2}-1.236857{col 44}{space 2} .3118646{col 55}{space 1}   -3.97{col 64}{space 3}0.000{col 72}{space 4}-1.848101{col 85}{space 3} -.625614
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2} .2235827{col 44}{space 2} .1871065{col 55}{space 1}    1.19{col 64}{space 3}0.232{col 72}{space 4}-.1431393{col 85}{space 3} .5903047
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2} .2284839{col 44}{space 2} .1870865{col 55}{space 1}    1.22{col 64}{space 3}0.222{col 72}{space 4}-.1381988{col 85}{space 3} .5951667
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2} .2951353{col 44}{space 2}  .184189{col 55}{space 1}    1.60{col 64}{space 3}0.109{col 72}{space 4}-.0658685{col 85}{space 3}  .656139
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.2212397{col 44}{space 2} .3069617{col 55}{space 1}   -0.72{col 64}{space 3}0.471{col 72}{space 4}-.8228735{col 85}{space 3} .3803941
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2} .3362689{col 44}{space 2} .2783973{col 55}{space 1}    1.21{col 64}{space 3}0.227{col 72}{space 4}-.2093797{col 85}{space 3} .8819176
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2} .3517792{col 44}{space 2} .6723974{col 55}{space 1}    0.52{col 64}{space 3}0.601{col 72}{space 4}-.9660955{col 85}{space 3} 1.669654
{txt}{space 30} {c |}
{space 16}china_net_mean {c |}{col 32}{res}{space 2}-.0276334{col 44}{space 2} .0039879{col 55}{space 1}   -6.93{col 64}{space 3}0.000{col 72}{space 4}-.0354496{col 85}{space 3}-.0198173
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}/cut1 {c |}{col 32}{res}{space 2}-1.785879{col 44}{space 2}  .400507{col 72}{space 4}-2.570859{col 85}{space 3}  -1.0009
{txt}{space 25}/cut2 {c |}{col 32}{res}{space 2}-.2679879{col 44}{space 2} .4029111{col 72}{space 4}-1.057679{col 85}{space 3} .5217035
{txt}{space 25}/cut3 {c |}{col 32}{res}{space 2} 1.629162{col 44}{space 2} .4046834{col 72}{space 4} .8359973{col 85}{space 3} 2.422327
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto m5a
{txt}
{com}. 
. 
. ologit supportopposeprc i.pid3 i.agecat  b2.gender_trinary i.education borninusa i.raceethn i.frenemy, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-3375.4469}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-3030.4385}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-3023.1338}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-3023.1193}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-3023.1193}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:2,497}
{txt}{col 57}{lalign 13:Wald chi2({res:20})}{col 70} = {res}{ralign 6:597.08}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-3023.1193}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1044}

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}              supportopposeprc{col 32}{c |} Coefficient{col 44}  std. err.{col 56}      z{col 64}   P>|z|{col 72}     [95% con{col 85}f. interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}pid3 {c |}
{space 18}Independent  {c |}{col 32}{res}{space 2}-.6714895{col 44}{space 2} .0999042{col 55}{space 1}   -6.72{col 64}{space 3}0.000{col 72}{space 4}-.8672982{col 85}{space 3}-.4756808
{txt}{space 21}Democrat  {c |}{col 32}{res}{space 2}-1.154809{col 44}{space 2} .1057609{col 55}{space 1}  -10.92{col 64}{space 3}0.000{col 72}{space 4}-1.362096{col 85}{space 3} -.947521
{txt}{space 30} {c |}
{space 24}agecat {c |}
{space 24}30-49  {c |}{col 32}{res}{space 2} .5584147{col 44}{space 2} .1327922{col 55}{space 1}    4.21{col 64}{space 3}0.000{col 72}{space 4} .2981468{col 85}{space 3} .8186825
{txt}{space 24}50-64  {c |}{col 32}{res}{space 2}  1.30743{col 44}{space 2} .1378637{col 55}{space 1}    9.48{col 64}{space 3}0.000{col 72}{space 4} 1.037222{col 85}{space 3} 1.577638
{txt}{space 26}65+  {c |}{col 32}{res}{space 2} 1.391047{col 44}{space 2} .1395405{col 55}{space 1}    9.97{col 64}{space 3}0.000{col 72}{space 4} 1.117552{col 85}{space 3} 1.664541
{txt}{space 30} {c |}
{space 16}gender_trinary {c |}
{space 25}Male  {c |}{col 32}{res}{space 2} .1324266{col 44}{space 2} .0759638{col 55}{space 1}    1.74{col 64}{space 3}0.081{col 72}{space 4}-.0164596{col 85}{space 3} .2813128
{txt}Non-binary / other definition  {c |}{col 32}{res}{space 2} .1180974{col 44}{space 2} .3856209{col 55}{space 1}    0.31{col 64}{space 3}0.759{col 72}{space 4}-.6377056{col 85}{space 3} .8739005
{txt}{space 30} {c |}
{space 21}education {c |}
{space 9}High school graduate  {c |}{col 32}{res}{space 2}-.2793102{col 44}{space 2} .2082603{col 55}{space 1}   -1.34{col 64}{space 3}0.180{col 72}{space 4}-.6874928{col 85}{space 3} .1288724
{txt}{space 6}Some college, no degree  {c |}{col 32}{res}{space 2}-.3197573{col 44}{space 2} .2121454{col 55}{space 1}   -1.51{col 64}{space 3}0.132{col 72}{space 4}-.7355548{col 85}{space 3} .0960401
{txt}{space 11}Associate's degree  {c |}{col 32}{res}{space 2} -.495207{col 44}{space 2} .2366679{col 55}{space 1}   -2.09{col 64}{space 3}0.036{col 72}{space 4}-.9590674{col 85}{space 3}-.0313465
{txt}{space 3}College grad/some postgrad  {c |}{col 32}{res}{space 2}-.4505109{col 44}{space 2} .2066714{col 55}{space 1}   -2.18{col 64}{space 3}0.029{col 72}{space 4}-.8555795{col 85}{space 3}-.0454423
{txt}{space 17}Postgraduate  {c |}{col 32}{res}{space 2} -.821928{col 44}{space 2} .2107877{col 55}{space 1}   -3.90{col 64}{space 3}0.000{col 72}{space 4}-1.235064{col 85}{space 3}-.4087917
{txt}{space 30} {c |}
{space 21}borninusa {c |}{col 32}{res}{space 2} .1878388{col 44}{space 2} .1303952{col 55}{space 1}    1.44{col 64}{space 3}0.150{col 72}{space 4}-.0677312{col 85}{space 3} .4434088
{txt}{space 30} {c |}
{space 22}raceethn {c |}
{space 11}Black non-Hispanic  {c |}{col 32}{res}{space 2} .3872882{col 44}{space 2} .1414368{col 55}{space 1}    2.74{col 64}{space 3}0.006{col 72}{space 4} .1100771{col 85}{space 3} .6644993
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2} .3025288{col 44}{space 2} .1272377{col 55}{space 1}    2.38{col 64}{space 3}0.017{col 72}{space 4} .0531474{col 85}{space 3} .5519102
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.0561198{col 44}{space 2} .2147484{col 55}{space 1}   -0.26{col 64}{space 3}0.794{col 72}{space 4} -.477019{col 85}{space 3} .3647793
{txt}{space 11}Asian non-Hispanic  {c |}{col 32}{res}{space 2} .3166948{col 44}{space 2}  .202562{col 55}{space 1}    1.56{col 64}{space 3}0.118{col 72}{space 4}-.0803194{col 85}{space 3}  .713709
{txt}{space 22}Refused  {c |}{col 32}{res}{space 2}-.2529061{col 44}{space 2} .5479621{col 55}{space 1}   -0.46{col 64}{space 3}0.644{col 72}{space 4}-1.326892{col 85}{space 3} .8210799
{txt}{space 30} {c |}
{space 23}frenemy {c |}
{space 19}Competitor  {c |}{col 32}{res}{space 2} .4295422{col 44}{space 2} .1571914{col 55}{space 1}    2.73{col 64}{space 3}0.006{col 72}{space 4} .1214526{col 85}{space 3} .7376317
{txt}{space 24}Enemy  {c |}{col 32}{res}{space 2} 1.431239{col 44}{space 2} .1702605{col 55}{space 1}    8.41{col 64}{space 3}0.000{col 72}{space 4} 1.097535{col 85}{space 3} 1.764944
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}/cut1 {c |}{col 32}{res}{space 2}-1.003446{col 44}{space 2} .2979196{col 72}{space 4}-1.587357{col 85}{space 3}-.4195339
{txt}{space 25}/cut2 {c |}{col 32}{res}{space 2} .5325137{col 44}{space 2} .2982387{col 72}{space 4}-.0520235{col 85}{space 3} 1.117051
{txt}{space 25}/cut3 {c |}{col 32}{res}{space 2} 2.450817{col 44}{space 2} .3020533{col 72}{space 4} 1.858804{col 85}{space 3} 3.042831
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. est sto m5b
{txt}
{com}. 
. ********************************************************************************
. *                                                       Visualization                                                                      *
. ********************************************************************************
. 
. * * * * * * Subgraph A / Panel A
. 
. coefplot (m1, label(Base, N = `m1_N')) (m2, label(Priority, N = `m2_N'))  ///
>         ,xline(0) drop(_cons) coeflabels(, labsize(vsmall)) ///
>         headings(       2.pid3 = "{c -(}bf:Party ID{c )-}"                                ///
>                                 2.agecat = "{c -(}bf:Age{c )-}"                                   ///
>                                 1.gender_trinary = "{c -(}bf:Gender{c )-}"                ///
>                                 borninusa = "{c -(}bf:Nativity{c )-}"             ///
>                                 2.raceethn = "{c -(}bf:Race/Ethnicity{c )-}"              ///
>                                 2.education = "{c -(}bf:Education{c )-}"                  ///
>                                 2.hhi_cat = "{c -(}bf:Household Income{c )-}"             ///
>                                 1.prcpower = "{c -(}bf:Limiting PRC Power Priority{c )-}" ///
>                                 1.GAP21Q40 = "{c -(}bf:PRC Relationship with USA{c )-}" ///
>         )                                                                                                       ///
>         xtitle("{c -(}it:OLS Coefficients{c )-}", size(vsmall))                                           ///
>         title("{c -(}bf:A:{c )-} {c -(}it:Support for International Student Enrollment{c )-}"       ///
>                 , pos(11) span justification(left))                     ///
>         subtitle("{c -(}stSerif:Do you think it's good or bad for U.S. colleges and universities to accept international students?{c )-}"                                         ///
>         , size(vsmall) pos(11) justification(left) span margin(0 0 2 0)) ///
>         text(10 .2 "{c -(}bf:Support{c )-}""international""students", size(small)) ///
>         text(14 -.2 "{c -(}bf:Oppose{c )-}""international""students", size(small)) ///
>         xsize(8) ysize(10) ///
>         legend(pos(11) ring(0) size(vsmall)) ///
> ///     caption("{c -(}stSerif:Pew Research Center, February 2021{c )-}", span size(small))       ///
>         name(g_general, replace) ///
>         addplot(pcarrowi 8.5 .1 8.5 .35, color(gray) || pcarrowi 12.5 -.1 12.5 -.35, color(gray))
{res}{txt}
{com}. 
. * * * * * * Subgraph B / Panel B
. 
. coefplot (m4, label(Base, N = `m4_N')) (m5, label(Priority, N = `m5_N')) ///
>         ,xline(0) drop(_cons) ///
>         headings(       2.pid3 = "{c -(}bf:Party ID{c )-}"                                ///
>                                 2.agecat = "{c -(}bf:Age{c )-}"                                   ///
>                                 1.gender_trinary = "{c -(}bf:Gender{c )-}"                ///
>                                 borninusa = "{c -(}bf:Nativity{c )-}"             ///
>                                 2.raceethn = "{c -(}bf:Race/Ethnicity{c )-}"              ///
>                                 2.education = "{c -(}bf:Education{c )-}"                  ///
>                                 2.hhi_cat = "{c -(}bf:Household Income{c )-}"             ///
>                                 1.prcpower = "{c -(}bf:Limiting PRC Power Priority{c )-}" ///
>         )                                                                                                       ///
>         xtitle("{c -(}it:Ordinal Logistic Coefficients{c )-}", size(vsmall))                                              ///
>         title("{c -(}bf:B:{c )-} {c -(}it:Limiting Chinese Students in U.S. Universities{c )-}"     ///
>                 , pos(11) span justification(left))                     ///
>         subtitle("{c -(}stSerif:When it comes to whether or not to limit Chinese students studying in the U.S., do you ...{c )-}"                                         ///
>         , size(vsmall) pos(11) justification(left) span margin(0 0 2 0)) ///
>         legend(pos(1) ring(0) size(vsmall)) ///
>         text(15 1 "{c -(}bf:Support{c )-}""limiting" "Chinese""students", size(small)) ///
>         text(22 -1.25 "{c -(}bf:Oppose{c )-}""limiting""Chinese""students", size(small)) ///
>         coeflabels(, labsize(vsmall))   ///
>         name(g_prc, replace) ///
>         addplot(pcarrowi 12.75 .4 12.75 1.6, color(gray) || pcarrowi 24.25 -.65 24.25 -1.85, color(gray))
{res}{txt}
{com}. 
. * * * * * * Combine and Export Final Picture
.         
. gr combine g_general g_prc, rows(1) ///
>         note("Priority models includes views on whether containing the power and influence of China should be a top priority or not. Question asked of half sample.""Robust standard errors and 95 percent confidence intervals shown.", span size(vsmall)) ///
>         caption("{c -(}stSerif:Pew Research Center, American Trends Panel Wave 82, February 2021{c )-}", span size(vsmall))
{res}{txt}
{com}.         
. gr export "${c -(}MyProject{c )-}/PAPER Figure 1.pdf", replace
{txt}{p 0 4 2}
file {bf}
~/Dropbox/0001 Academic Projects/Completed/0171 Parasecurity and Education/Replication/PAPER Figure 1.pdf{rm}
saved as
PDF
format
{p_end}

{com}.         
. 
. * * * * * * Appendix Table
.         
. esttab m1 m2 m3a m3b m4 m5 m5a m5b using "${c -(}MyProject{c )-}/APPENDIX Table A3.tex"           ///
>         , replace longtable lab nobase noomit compress nogap                                            ///
>         title("Pew Full Results for International Enrollment \label{c -(}tab:pewresults{c )-}") ///
>         nonotes addnote(Robust standard errors.)                                                                        ///
>         drop(*cons* *cut*)                                                                                                                      ///
>         b(%9.2f) se(%9.2f)                                                                                                                      ///
>         star(+ 0.10 * 0.05)                                                                                                                     ///
> refcat( 2.pid3 "\textit{c -(}Party ID{c )-}"                              ///
>                 1.gender_trinary "\textit{c -(}Gender{c )-}"              ///
>                 borninusa "\textit{c -(}Nativity{c )-}"                   ///
>                                         2.agecat  "\textit{c -(}Age{c )-}"                                        ///
>         2.raceethn "\textit{c -(}Race/Ethnicity{c )-}"    ///
>                 2.education "\textit{c -(}Education{c )-}"                ///
>                 2.hhi_cat "\textit{c -(}Household Income{c )-}"   ///
>                 1.prcpower "\textit{c -(}Limiting PRC Power Priority{c )-}"       ///
>                 , nolabel)      ///
>                 mtitle("International" "International" "International" "International" ///
>                         "PRC" "PRC" "PRC" "PRC")
{res}{txt}(output written to {browse  `"~/Dropbox/0001 Academic Projects/Completed/0171 Parasecurity and Education/Replication/APPENDIX Table A3.tex"'})

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/rpm47/Dropbox/0001 Academic Projects/Completed/0171 Parasecurity and Education/Replication/LOG Analysis Pew 2025 09 22.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 Sep 2025, 16:36:46
{txt}{.-}
{smcl}
{txt}{sf}{ul off}